Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Self-training is a commonly semi-supervised learning Algorithm framework. How to select
the high-confidence samples is a crucial step for algorithms based on self-training …

Semi-supervised learning (SSL) based on manifold regularization in many fields has
attracted widespread attention and research. However, SSL still has two main challenges …

Recently, safe semi-supervised clustering (S3C) has become an emerging topic in machine
learning field. S3C aims to reduce the performance degradation probability of wrong prior …

The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-
means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of …

Data partition with high confidence is one of the main concentration of researchers in Soft
Computing for many years. It is known that there may be some data with less confidence …

Controlling the impact of partial supervision on the outcomes of modeling is of uttermost
importance in semi-supervised fuzzy clustering. Semi-Supervised Fuzzy C-Means …

Existing pre-processing methods for the prior membership degree matrix suffer from the
following issues:(1) The labeling constraints for prior membership degree matrix have an …

Fuzzy clustering is a simple but efficient clustering method, which aims to deal with
ambiguous and overlapping data classification boundaries and provide detailed …

Clustering is a crucial method for deciphering data structure and producing new information.
Due to its significance in revealing fundamental connections between the human brain and …